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A User Modeling Approach to Support Knowledge Work in Socio-computational Systems

  • Karin Schoefegger
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6787)

Abstract

The rise of socio-computational systems such as collaborative tagging systems, which rely heavily on user-generated content and social interactions, changed our way to learn and work. This work aims to explore the potentials of those systems for supporting knowledge work in organizational and scientific domains. Therefore, a user modeling approach will be developed which enables personalized services to shape the content towards individual information needs of novice, advanced and experienced knowledge workers. The novelty of this approach is a modeling strategy which combines user modeling characteristics from distinct research areas, the emergent properties of the socio-computational environment as well as non-invasive knowledge diagnosis methods based on the user’s past interaction with the system.

Keywords

User modeling emergent semantics work-integrated learning personalized services collaborative tagging systems knowledge work 

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References

  1. 1.
    Brusilovsky, P., Sosnovsky, S., Yudelson, M.: Adaptive Hypermedia Services for E-Learning. In: Workshop on Applying Adaptive Hypermedia Techniques to Service Oriented Environments at the Third International Conference on Adaptive Hypermedia and Adaptive Web Based Systems, pp. 470–479 (2004)Google Scholar
  2. 2.
    Budura, A., Bourges-Waldegg, D., Riordan, J.: Deriving Expertise Profiles from Tags. In: CSE 2009: Proceedings of the 2009 International Conference on Computational Science and Engineering, pp. 34–41. IEEE Computer Society, Washington, DC (2009)CrossRefGoogle Scholar
  3. 3.
    Fu, W.-T., Dong, W.: Facilitating Knowledge Exploration in Folksonomies: Expertise Ranking by Link and Semantic Structures. IEEE, Los Alamitos (2010)Google Scholar
  4. 4.
    Golder, S.A., Huberman, B.A.: The Structure of Collaborative Tagging Systems. Journal of Information Science (2006)Google Scholar
  5. 5.
    Kang, R., Fu, W.-T., Kannampallil, T.G.: Exploiting knowledge-in-the-head and knowledge-in-the-social-web: effects of domain expertise on exploratory search in individual and social search environments. In: Mynatt, E.D., Schoner, D., Fitzpatrick, G., Hudson, S.E., Edwards, K., Rodden, T. (eds.) CHI, pp. 393–402. ACM, New York (2010)Google Scholar
  6. 6.
    Ley, T., Seitlinger, P.: A Cognitive Perspective on Emergent Semantics in Collaborative Tagging: The Basic Level Effect. In: Proceedings of International Workshop on Adaptation in Social and Semantic Web (SAS-WEB 2010), pp. 1–10 (2010)Google Scholar
  7. 7.
    Lindstaedt, S.N., Beham, G., Kump, B., Ley, T.: Getting to Know Your User – Unobtrusive User Model Maintenance within Work-Integrated Learning Environments. In: Cress, U., Dimitrova, V., Specht, M. (eds.) EC-TEL 2009. LNCS, vol. 5794, pp. 73–87. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  8. 8.
    Micarelli, A., Gasparetti, F., Sciarrone, F., Gauch, S.: Personalized Search on the World Wide Web. In: Brusilovsky, P., Kobsa, A., Nejdl, W. (eds.) Adaptive Web 2007. LNCS, vol. 4321, pp. 195–230. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  9. 9.
    Schoefegger, K., Seitlinger, P., Ley, T.: Towards a user model for personalized recommendations in work-integrated learning: A report on an experimental study with a collaborative tagging system. In: Proceedings of the 1st Workshop on Recommender Systems for Technology Enhanced Learning (RecSysTEL 2010), vol. 1, pp. 2829–2838. Procedia Computer Science (2010)Google Scholar
  10. 10.
    Tomuro, N., Shepitsen, A.: Personalized Search in Folksonomies with Ontological User Profiles. In: Proceedings of the International Joint Conference Intelligent Information Systems, pp. 1–14 (2009)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Karin Schoefegger
    • 1
  1. 1.Knowledge Management InstituteGraz University of TechnologyGrazAustria

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